Knowledge hiding and occupational stress affecting employees’ performance: comparative analysis from emerging and advanced economies
Bibliographic record
Abstract
This study examined the impact of knowledge hiding (KH) and occupational stress (OS) on the employees’ performance (EP) within the logistics businesses in the contrasting economies: Canada, United Kingdom, India, and Pakistan. The matrix-based questionnaire circulated and total 224 valid responses (56 from each country) out of 408 sample gathered through networking, disproportionate quota, and purposive sampling technique. For the data analysis, PLS-SEM was employed. The results showed that knowledge hiding and occupational stress affect the performance of the employees negatively in all considered distinctive economies. Interestingly, the usage of funnel approach revealed that higher knowledge hiding (KH) is evident in Canada and the UK (developed economies) as compared to Pakistan and India (emerging economies). The comparison revealed that the knowledge hiding is higher than the occupational stress in affecting the employees’ performance. Furthermore, knowledge hiding creates unfriendly environment with higher depression and anxiety, that are contributing factors towards lower performance at all levels of the organisation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".